Student volunteers: why hospitals must invest in their futures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As the current volunteer work force ages, hospitals are faced with the challenge of evolving their student volunteers into active adult volunteers. Engaged student volunteers may be nurtured by the hospital to become future employees, links to the community or potential donors. Currently, retention rates among student volunteers indicate that once the majority of students begin post secondary education, they discontinue their association with the hospital. Using a scientific marketing research approach, this paper addresses three questions aimed at producing a model to increase long‐term retention among student volunteers. Why do seemingly committed volunteers discontinue their association with the hospital? How does the hospital develop a system that allows and encourages students to maintain contact with the hospital? How can a hospital integrate a virtual volunteering model into its traditional volunteering model? The conclusions lead the reader to reassess the way they view student volunteers and strongly encourage the reader to view the students not just as volunteers, but also as long‐term potential active members of the hospital community.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it